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Posted to issues@spark.apache.org by "Hyukjin Kwon (JIRA)" <ji...@apache.org> on 2018/02/11 08:33:00 UTC
[jira] [Resolved] (SPARK-23314) Pandas grouped udf on dataset with
timestamp column error
[ https://issues.apache.org/jira/browse/SPARK-23314?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon resolved SPARK-23314.
----------------------------------
Resolution: Fixed
Fix Version/s: 2.3.0
Issue resolved by pull request 20537
[https://github.com/apache/spark/pull/20537]
> Pandas grouped udf on dataset with timestamp column error
> ----------------------------------------------------------
>
> Key: SPARK-23314
> URL: https://issues.apache.org/jira/browse/SPARK-23314
> Project: Spark
> Issue Type: Sub-task
> Components: PySpark
> Affects Versions: 2.3.0
> Reporter: Felix Cheung
> Assignee: Li Jin
> Priority: Major
> Fix For: 2.3.0
>
>
> Under SPARK-22216
> When testing pandas_udf on group bys, I saw this error with the timestamp column.
> File "pandas/_libs/tslib.pyx", line 3593, in pandas._libs.tslib.tz_localize_to_utc
> AmbiguousTimeError: Cannot infer dst time from Timestamp('2015-11-01 01:29:30'), try using the 'ambiguous' argument
> For details, see Comment box. I'm able to reproduce this on the latest branch-2.3 (last change from Feb 1 UTC)
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